Digital image capture has received widespread acceptance by users of image capture equipment. Both still and video digital cameras, which use solid-state imagers or image sensors to capture images, are presently in widespread use. Common solid-state imagers, which have an array of pixels for image capture are based on CCD and CMOS technology, as well as others.
One problem confronted by solid-state imagers is the presence of noise in the captured image, particularly under low-light conditions, and/or where an ISO setting of a camera is set high, resulting in shorter image integration times. Image noise appears in a displayed or a printed image as a graininess in the image.
Past image capture processing approaches have been used in an attempt to reduce image noise. Such approaches typically rely on a softening, or blurring, of the overall image. However, such techniques may soften the entire image, even in areas of the image where noise is not present or is at least visually imperceptible. Moreover, softening of the overall image—analogous to a small defocusing of an imaging lens—reduces image sharpness, which is typically undesirable.
Color imagers typically output red/green/blue (RGB) color signals from a pixel array, since each imager pixel is typically covered by one of a red, green, or blue filter; arranged, for example, as in a Bayer pattern, illustrated in FIG. 1. To obtain red, green, and blue information for each pixel of an image, color interpolation is needed. This process, known as “demosaicing,” analyzes the color values of appropriate neighboring pixels to estimate, in effect, each pixel's unknown color data. If one of the green pixels on a RGRG sequence line of the Bayer pattern (e.g., the third line of the array 82 of FIG. 1) is being read out, the process of color interpolation estimates the pixel's blue value by looking at the values of the blue pixels above and below it, and combining those blue values. For the red color estimate, the process looks at the values of the red pixels to the left and right of the green pixel and combines those values.
The captured image is subject to blur due to various causes, including a limited resolution of the camera lens, inter-pixel cross-talk in the sensor array and various image processing that adds intentional or unintentional blur. Aperture correction sharpens or “deblurs” the image. The sharpening is performed by increasing the gain of high frequency components of the image. While this makes the overall image sharper, it also accentuates any noise in the image, since random pixel-to-pixel variations are amplified, as well.
The RGB values, determined during demosaicing, may be converted to YUV values, where Y is a luminance value and UV are chrominance values for additional pixel processing. Conventional aperture correction involves comparing the difference in a luminance value of the target pixel and its surrounding pixels to a preset threshold value, set during manufacturing. If the threshold value is exceeded, the target pixel is subjected to aperture correction, e.g., increase of that difference, at a preset level. Such conventional systems select the threshold value to minimize visible noise in areas of all levels of brightness. This may result in excessive blur of dark areas, while achieving only moderate noise suppression in bright areas.
Accordingly, there is a need and desire to more selectively reduce noise in a captured image in accordance with detected image noise parameters.